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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1171–1180 of 2050 papers

TitleStatusHype
Towards a Theoretical Framework of Out-of-Distribution Generalization—0
AI without networks—0
Encoding-dependent generalization bounds for parametrized quantum circuits—0
Neural Active Learning with Performance Guarantees—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Network Estimation by Mixing: Adaptivity and More—0
Robust Model Selection and Nearly-Proper Learning for GMMs—0
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical TextsCode0
Morphological Segmentation for SenecaCode0
Corpus-Based Paraphrase Detection Experiments and Review—0
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